7 Steps to Marketing Nirvana – The Ultimate How-To Guide [Step 2]

Transforming Your Data into a Marketing Plan

At this stage along the path to Marketing Nirvana, all of your POS and transactional data have been consolidated and filtered through PMA software.

This means that all of your retail information has been stitched together across all available channels – generating the template for the omnichannel, 360-degree customer view that the mega-retailers use today.

By using this comprehensive view of the customer, marketers can build their marketing plans with greater insights to address media spend, price promotions, special campaigns, and specific strategies for segments of their customer and prospect universes.

Insights include answers to the following questions:

Who are my customers?

Where can I find them?

What message do I deliver?

Which channel(s) should I use?

Who Are Your Customers?

The first plan of action is to enhance your customer transaction information with information that can help you build a marketing plan. List brokers, data brokers, and major compilers each offer hundreds of different customer attributes (or variables) for marketers to choose from.

Three critical types of actionable intelligence are available (from different sources, offering differing degrees of customer coverage):

Geographic/Demographic – income, age, region, dwelling location/type.

Psychographic – lifestyles, interests, hobbies.

Behavioral – actual buying behavior/purchasing tendencies.

These different types of data offer marketers the essential raw materials to build a rich understanding of who their customers are and how best to communicate with them. There are thousands of permutations that can potentially be of value.

As you would expect, all of these variables have a cost associated with them. The data is not free.

Therefore, understanding how to use all of this information both wisely and cost-effectively can quickly become overwhelming – especially with such a vast range of personalized variables for sale throughout the marketplace.

But if you’re following these steps, there is no need to worry. PMAs takes care of the hard work for you by finding segments of customers that behave similarly and profiling them for you.

In other words, PMAs automatically find groups (or clusters) of customers that help divide your total market into segments that behave in similar ways with respect to your product offering.

Market Segmentation

A logical question at this point is, how do PMAs help you arrive at the best segmentation solution for your total market? A common technique deployed is a statistical method called cluster analysis.

Cluster analysis creates groups of customers based on maximizing homogeneity within groups and heterogeneity between groups of customers.

Each of these groups (or segments) can provide extremely practical information about your customers’ lifestyles, lifestages, incomes, interests, and – perhaps most importantly – the likelihood that they will purchase your products in the future.

There are many marketers who are not familiar with 3rd party data solutions. Even those who have some rough familiarity have likely used them less than they would have liked because of their high historical costs. However, in today’s world of “big data,” those costs have gone down significantly.

With all of this precious information out there, retailers need to seriously consider how to take advantage it.

Theoretically, you could peruse through all of the data available on the planet and create a custom cluster analysis that points to which variables most closely match your ideal customer base.

However, while some very large companies pursue this path, such a method is time consuming, expensive, and (worst of all) potentially wasteful if you wind up making poor decisions along the way.

Through its efficient utilization of third party data resources, a PMA makes your data smarter via perceptive insights and practical profiles through the distillation of demographic and psychographic database attributes.

By already pre-clustering all of the consumers in the US specifically for you and your business, you have a running start on better understanding your customer universe without the high costs of distilling a custom solution.

Instead, you can spring into action right away. And, you can always add new data to your database later if it makes sense to do so.

Lifestage Migration

Powerful marketing opportunities arise when a member of your target audience transfers (or migrates) from one lifestage to another.

Your odds of customer acquisition dramatically increase when equipped with a repository (PMA) that can keep track of these shifts, respond to triggers, and autonomously deliver timely, personalized offers.

An example of lifestage migration is when an individual or couple moves from a rental apartment to a single-family home for the first time.

According to a NAHB study, buyers of new homes outspend non-movers by 2.6 times on such items as appliances, furniture, and remodeling.

Wouldn’t it be great to be able to send a timely, personalized discount offer for new furniture, kitchen appliances, roofing, landscaping products, etc. to someone you know for a fact is a new/prospective homeowner?

Well, it turns out that you can.

That ability alone gives you a significant competitive edge over the rest of the competition.

Actionable Segmentation and 1-to-1 Marketing

As with any marketing segmentation solution, the goal is to create a set of actionable segments that are both well understood by your organization and stable over time.

These segments typically consist of a set of available buyer information (including external data and product information) that allows you to better engage customers, automate the right messages to send, further understand the buyer lifecycle, and create new customer journeys.

For example, there can be multiple categories of clearly distinct populations that shop at a particular retailer.

If you want to get the most out of each group, you must market to each separately with deliberately distinct, exclusive messages.

When initiating any form of customer communication, your message should be as relevant as possible. This ensures firm engagement and an increased likelihood to respond.

In the retail example, a younger demographic might be much more receptive to sales on the latest fashions, while an older demographic might be more inclined to lean toward more traditional, conservative brand offerings. In turn, target your messaging to those who are most likely to be receptive.

Thorough cluster and lifestage development allows marketers to identify the natural pockets of opportunity for each customer without overwhelming them with unnecessary noise.

Blindly blasting universal messages to all sectors of your base will skyrocket the amount of irrelevant messages delivered. At best, those messages will be ignored. At worst, it could turn customers off from your brand altogether.

Execution Considerations

While developing multiple segments for a retailer, the approach should always be as pragmatic as possible.

Start by asking yourself a few questions:

How many segments can your organization support?

How many segments will allow you to maximize marketing effectiveness?

The answers can get tricky, because when a retailer deals with multiple segments, they should work to tailor their approach differently for each one. While there isn’t one set of answers that works for everyone, the considerations are the same for all companies.

Let’s say you sell food products. One segment of your base is vegetarian, one is gluten free, the second is both, and the third is neither. Each group should receive very different sets of promotions and recommendations.

This costs time, money, and resources to develop.

When you begin to grow and expand the number of segments you are marketing to, you also increase the amount of work necessary to cater to each one. This can get overwhelming fast, particularly for smaller retailers.

This leads to a key challenge – figuring how many segments are necessary to maximize effectiveness, while weighing that against the internal cost of maintaining the amount of segments and messages.

A PMA helps solve that quandary by providing swift personalization for each identifiable segment.

Regarding email messages, PMAs can deploy personalized, one-to-one blocks of relevant communication autonomously. This allows smaller retailers to be more productive in their marketing efforts across multiple segments with less overhead and greater traction.

Again, the heavy lifting is already done for you.

Where Do Your Customers Come From? How Do They Purchase Your Products?

Many small and medium sized businesses lack the capacity to track the range of factors leading up to the first point of contact with each customer.

This is a shame, because knowing where and how a customer relationship started may actually be more important than knowing the point of purchase itself.

With a PMA, you can capture valuable customer acquisition information, connect all of the dots together across channels, and build a strong data foundation going forward.

Acquisition Channel Analysis

The acquisition source – or the method/channel through which a customer relationship was initially formed – is highly predictive of how customer behavior will evolve down the road.

Say a buyer first came into contact with your company through your website. Perhaps they arrived at your site in the first place by clicking on a social media ad. From there, he/she subscribed to your email list and purchased a product on their iPhone with a discount code.

Meanwhile, another buyer actually walked into one of your retail stores and used a physical coupon from a mail-order catalog.

Wouldn’t you develop your discourse with the two differently? Certainly.

If you have a group of customers who are online-only shoppers, stick to the digital realm. If others are frequent in-store shoppers, focus on that realm, but still always leave the option to purchase online as well.

By understanding which communication channels are most important to each customer in your base, you can develop the right respective combination of media to reach them through.

This practice helps to determine where to invest your time and money, reduce wasted efforts, maintain relevance, and optimize Customer Acquisition Cost (CAC).

From there, you can lead them further along the right path on the customer journey by establishing the best messages to entice subsequent purchases.

This type of predictive element is extremely valuable. After all, the ability to accurately forecast what buyers are going to do next is something that all marketers strive for.

What Products Do They Purchase?

Obviously, you can glean a great deal of customer knowledge by tracking what types and categories of products they buy. From there, the objective is to maximize their lifetime value through new purchases and services down the road.

Market Basket Analysis

Market Basket Analysis is a form of statistical analysis used to determine which products customers normally purchase together.

For example, someone who purchases a surfboard is likely to buy another related item/accessory in conjunction – such as a wetsuit.

By understanding how products are purchased together, marketers can guide the dialog with customers in a way that is both helpful to the consumer and profitable to the company.

Another example could be after someone who purchases a particular brand of electric guitar, carefully tailored suggestions would arise suggesting typical pairings with certain amplifiers or effects pedals.

Meanwhile, a poor example of this method would be if a customer who just bought a lawnmower received subsequent suggestions about other lawnmowers instead of other supplementary products like hedge trimmers or leaf blowers.

Mistakes in the form of duplicate or irrelevant pairing suggestions dramatically decreases your chance of adding items into your customer’s cart, and can even damage your reputation in the buyer’s eyes.

Top retailers who successfully implement this practice consistently – such as Amazon – are able to maximize both quantity and price of each purchase through this practice.

This is because they had the resources to develop high-end software capabilities necessary to do so. Without a similar toolset (like those found in a PMA), how could others expect to do the same?

Cross Category/Single Category

How broad or narrow are your buyers’ interests?

If you sell winter sporting gear, an example of a single category buyer would be an individual who only purchases skiing equipment.

Meanwhile, a cross-category buyer would be someone who buys both skiing and snowboard equipment.

For instance, if you know a buyer is only interested in skiing, you would know to not waste your time suggesting anything beyond that category – even if it is technically something similar like snowboarding.

Accurately differentiating between various cross and single-category buyers serves to further personalize the offers you send to them. Is someone a hiker, fisher, and a camper? Or just one of those?

This is just another example of a simple, yet powerful concept and practice that are often overlooked by retailers.

What Are Their Needs [And How Can We Meet Them]?

Ultimately, marketing is about people, not products. Therefore, it is extremely important for retailers to understand the customers’ requirements for their goods and services.

How do customers want to be viewed and treated by the brands they purchase from? And, how does your product meet their needs better than the alternatives in the marketplace?

Simply speaking the customer’s language and catering to their needs accordingly holds a great amount of weight.

For instance, there are certain people who need to feel like their products are trending. Those who want the latest and greatest often desire feelings of prestige and recognition from both their sellers and their peers.

Some also wish to be viewed as insiders with access to exclusive offers, products, and treatment that others do not receive.

Meanwhile, there are others who always want to feel like they are getting the best deal possible. Focus on providing them with information on your latest sales and discounts.

With the proper tools necessary to conduct proactive data and survey analyses, you can easily understand the needs profile of each particular customer segment and design your mode of communication with each cohort differently.

Understanding the Buyer Lifecycle

The term Buyer Lifecycle (BLC) refers to the natural progression of a customer/retailer relationship. It depicts your customer’s relationship and brand engagement from the moment that they become a prospect all the way up until their final purchase.

Every customer has their own unique lifecycle throughout their relationship with your company. A paramount goal of your brand should be to work to both discover customer behaviors while also influencing and creating new ones.

With PMAs, such actions are swiftly automated via predictive analytics and machine intelligence. They analyze the discrete events and metrics that either drive or diminish both revenue opportunity and customer value.

The notion that any customer file or database is mostly static is a misconception. In reality, your customers’ BLC is a dynamic, ongoing process that changes every single day.

A PMA utilizes email behaviors, past purchases, web behaviors and external data sources to understand how engaged certain customers and prospects are at any given time.

1. Prospects

Not yet customers.

2. Actives

Individuals currently engaged and/or spending with you.

3. In Market

Buyer currently shopping for your products and are prepared/likely to buy again

4. Faders

Subjects no longer purchasing at the rate their customer profile suggests they can.

5. At Risk

Buyers most likely to stop spending with your brand and fall into attrition.

6. Inactives

Customers who have ceased purchasing your products.

This allows you to see exactly where your current/potential customers are by autonomously designating them into one of six distinct stages.

The Buyer Lifecycle

Example of a Buyer Lifecycle Analytics (BuyerGenomics).

PMA’s freely shift each customer among the six different stages according to a range of established variables and key signals:

How often have they visited your website? How long since the last time?

What did they browse? How close did they come to making a purchase?

What are their respective buying patterns? How frequently/infrequently have they bought?

Are they opening/clicking your messages? Or ignoring them?

Have they made a trip to one of your stores?

Due to advances in cloud computing, it is finally feasible for all marketers to maximize BLC visibility so that swift action can be taken whenever crucial shifts between stages occur.

For instance, a customer who recently shifted into the “Fading” stage requires a form of marketing intervention. This can come in the form of special discounts, privileges, gifts, or other offers that may reactivate the customer.

Meanwhile, for customers who have been labeled “Inactive” for an extended period of time, it may not make sense to waste anymore of your marketing resources on them.

On the other end, once a “Prospect” shifts to “Active,” you have your best shot at convincing them that your product is the one they want to buy repeatedly.

Send them a personalized message, and strike while the iron is hot.

Each sector of the BLC involves a different approach and strategy, and a proper grasp of its intricacies help to better understand each customer and consistently target them with relevant information.

Conclusion

It is always the marketer’s goal to understand the triggers that are likely to create sales in order to build successful campaigns.

The best way to do this is to know who your customers are, where they come from, what their interests/inclinations are, which channels they frequent, the types of products they buy, and how they want to be treated.

A PMA platform stacks the deck in your favor by giving you a cost-effective repository of marketing information that supports smart, sophisticated segmentation right from the moment your rudimentary transactional/POS data is uploaded (see Step 1).

Therefore, not only can you possess an advanced, expansive database that captures and categorizes comprehensive customer information in real-time, you also have access to vast amounts of previously inaccessible discrete demographic, psychographic, and behavioral information.

All of these factors combine to formulate the core components of each individual customer’s genetic code – or Customer Genome℠.

Now that you’ve put in the effort to know who your customers are, you have the ability to apply that information towards building intelligent, targeted, personalized, cost-effective marketing plans and strategies.

This leads us to the third phase of our series – learning how to dig deeper and narrow the scope even further to Create Customer Journeys and Segmentation Strategies.

About the Author:

Gary BeckChief Strategy Officer

Gary’s background includes over 30 years of analytics & database innovation for several leading Fortune 500 companies and Madison Avenue advertising agencies. Gary has been a frequent lecturer and author on the topics of database marketing and applied statistics. His articles have been published in DM News, Direct Marketing and the Journal of Direct Marketing. He recently was President of the Direct Marketing Idea Exchange and served on their Board. Gary received his M.S. in Industrial Administration from Carnegie Mellon University.

Any further questions or insight? Email Gary at gbeck@buyergenomics.com.

A MARKETER’S DILEMMA: HOW DO YOU
CHOOSE WHAT TO AUTOMATE?

Summary:

This is our last episode of 2018, and we’re thinking ahead to next year. One of the big questions is what are we going to automate in 2019? Stephen Yu, Chief Product Officer at Buyer Genomics, joins us again to discuss the importance of planning and setting specific goals when it comes to automation. We’ll talk about both the benefits and the limitations of automation, and how you can combine automation with a human function to get good results.

Below is a lightly edited transcript of Episode 34 of the Inevitable Success Podcast with Damian and special guest Stephen Yu. (Listen Here)

Transcript:

Damian: We’re thinking a lot about what we’re going to be doing in 2019, and one of the things that came up is what are we going to automate in 2019? Stephen, you had a very interesting response to that, which was…?

Stephen: Do you know what you’re automating? You automate things that you know how to do already. Automation is not about creating something out of nothing. That means you need to have a goal first. We talked about that when we talked about the benefits of modeling in general. Modeling doesn’t give you any answers—machine learning, or AI, or human-made models—it doesn’t matter what it is. You have to know what you want out of it, and deciding that is a uniquely human function. Nobody is going to do it for you.

Damian: Yea and one of the other things that I thought was interesting is if you try to automate things you really don’t know the answer to, who knows what the expected outcome is? What could you expect as the outcome? It would probably be bad.

Stephen: That’s right. The whole of automation is really two-fold. One is to cut time so that we do things faster. Two is to do it with fewer people. Not good news for a lot of the workforce out there, and there’s a reason why a lot of publications talk about how many jobs will be gone if AI takes over. Some of them are total science fiction, but some of them are not unfounded. A lot of people will lose jobs. Let’s face it, though—we automate things to cut time and to cut human resources. That’s it.

Damian: You know what’s interesting—to kind of flip that on its head a little bit—there are so many businesses out there that have one or two-person marketing teams, and their revenue doesn’t justify having more than that, regardless of whether they are amazing or not. I actually think that if a person really studied and became a student of how to be a good automator, that’s a massive opportunity, because then they could say, “I’m the guy (or the girl) who can do the job of ten people by myself, because I have this kind of technology.”

Stephen: But let’s say that you’re a marketer, and you have a lot of jobs or things to do on your list. You have to break it down from the point of view of not just the things you don’t want to do, but also what is the most time consuming and what is repetitive. You automate those things first.

Damian: That’s a great thing. I think that’s something you should write down. Stop and think about what you did in 2018 (or any year), and think of the things you did over and over again. Those are repetitive tasks, so can you be thinking, write down some rules, find some sort of technology, to outsource those tasks to a machine.

Stephen: Now, if you decide that a task is right for automation, then what’s next?

Damian: As I was saying that, I actually think that one of the next questions to ask would be what are all the things I was supposed to do, but didn’t do? Because I probably won’t do them again next year.

Stephen: Well, if this is about writing a new procedure for something, then a machine is not going to do that for you; you have to do that yourself. Now, let’s say that you isolated a task or a bunch of tasks that you want to automate. Now you have to think like a machine, even if you’re not a coder. So, imagine you have thousands of offer codes, and it’s in really bad shape. You want to automate it, because you don’t want to go line by line and clean it up yourself. Well, there has to be some logical way to express that command, otherwise, no one can program it.

Even if you use the pattern recognition module of machine learning, you still have to teach the machine what it’s supposed to clean up. You have to do it in a way that converts your thoughts into logical steps. I heard about a bunch of enthusiastic young mothers who now not only teach their kids foreign languages and math skills early on, but some of them also wanted to teach them how to code. It’s a very noble idea. Do you know how they teach code to four or five-year-old= kids?

Damian: I’m very interested in this for two reasons, one because my first-born could be born at any minute—we’re shooting for the next couple hours, but it’s probably not going to happen—but the other reason, and I’ve said this a bunch of times, is that I think it’s an amazing thing to learn as a kid. So how do they teach it?

Stephen: They take a task of say creating ramen noodles. You want a machine to open up a packet of instant ramen noodles, put it in boiling water, and start cooking at the right temperature. You know how to do it, because you’re a human being. You know when it’s done. But let’s say that machine has no idea what cooked ramen looks like. Write an instruction from step one all the way to however many steps it takes. That is the first lesson in coding. What would be the first task, do you think, if you were making a packet of ramen noodles? Let’s say that you have pots and pans and a packet of ramen. What would be the first step?

Damian: So, I have all the ingredients? Oh man, this is a pop quiz.

Stephen: Yea, you just have to do it. There’s no right or wrong answer. By the way, a machine should be able to do it.

Damian: All right, I think it’d be something like, “Reach out with your dominant hand.”

Stephen: Well, a machine doesn’t have a hand!

Damian: Well, there you go.

Stephen: Let’s just say such a machine exists. I would say that you have to talk about the measurement of water. How much water do you need? Let’s say 450 mL. Then when does it boil? Up to what point? You have to teach the machine when the boiling point is, so that means you need the module that measures temperature, or some observation like there are a bunch of bubbles coming off the water. Now it’s boiling. What do you put in? Open the packet. How do I do that? Cut it from the top, from the bottom, sideways, or look for some indication of a cutting line?

Damian: Wow, we somehow turned this into a cooking show, I don’t know how we did it.

Stephen: Yea, because everybody can relate to this. The point is, let’s say that your task is to clean dirty data—I was just looking at some data this morning, and do you know how many variations of Facebook I saw there? Facebook is a good source, right? So, can you imagine all the variations I saw? You could have Facebook with a lowercase, it could be www.facebook.com, it could be m.facebook.com, or it could simply be FB, etc. The point is, now you’re not doing it, the machine is. Now you have to think like we did with the packet of ramen. Where do you start? Is that FB a combination, or do I give an example of what FB could be, or let the machine just do some deep learning? If it’s learning, you have to tell them FB is right and FBC is wrong, because the machine is not going to know.

Damian: That rule could be used in a lot of places, because most often, when I’ve encountered the need to write something like that, it’s been for reporting. I actually think that’s a great thing to automate if you can, because typically that’s something you know, and it’s repetitive, and sometimes it’s something you should be doing but aren’t doing, and sometimes it’s something you really shouldn’t be spending so much time doing, but you are.

Stephen: That’s right. You also have to think like a machine does. What if none of the rules that you give to a machine capture every error that there is, and yet you even have to think about the fact that if you do all these things in a very exhaustive way, and you still have some things to clean up, call operations, or whatever. But if you just don’t say it, the machine is not going to do it for you. You have to say it explicitly.

Damian: Yea, I definitely think there’s a place for troubleshooting and Q&A (quality assurance) on anything you go to automate. It’s actually a good point. When you’ve automated something that is not a last step, you need to look at that.

Stephen: That’s absolutely right. There’s a meme floating around—let me paraphrase it, because I don’t remember exactly. Do you know the difference between machine learning and deep learning and AI? Very simply, AI will correct their own errors, whereas machine learning still needs human beings to correct them. It’s a very simplified version of the explanation, but we use these words interchangeably anyway, and probably they’re not wrong. For a layman, who cares, as long as they’re not the one doing it.

But the point is, you are the one with the goal for what the machine is supposed to do. Is it about finding some patterns that are useful for a future sale? Is it about building an actual model to predict who is going to be the most valuable customer? Or is it about sorting things so you don’t have to sort them to find the ten most valuable leads? What is the goal? It’s not about math or whatever.

You have to have the goal, that’s number one. And number two is to determine if this repetitive and automatable? Let’s say that finding the best lead is the goal. Let’s say the machine produced a lot of high scores. From the machine’s point of view, with the data it has, that’s all it could do. Now you have to break the tie—how do you do that? In that case, there is human intervention. A person ultimately makes the decision based on the results the machine produces.

So that is the point: you don’t have to be the coder, but you still have to think in terms of a coder and think about what the machine needs to break things down. Think logically in terms of how you are going to instruct the machine, and do you have all the ingredients to do full automation. That would be my advice to marketers who want to go to the next level with AI to make their businesses faster and better. When it comes to what the machine is actually supposed to do, you have to think about it for awhile.

Damian: I agree. In closing, I would strongly encourage, if not challenge, the listener to write down two or three things that you plan to intelligently automate in the coming year. Then think about what you are going to do with all that time you just opened up, because that is where the ROI is.

Stephen: Right, well, there’s no shortage of work I hope.

Damian: Well, if you just saw automate what you did last year, and then don’t do anything with that freed up time, you’re going to get a flat ROI. You’ll have more time, maybe your golf game will get a little bit better.

Stephen: Hopefully you’ll think a little more about marketing, but you know, you’re right. There’s a joke among coders that the laziest coders write the best macros for automated modules because they don’t want to do it again. So, some laziness is a good motivation for automation, yes, but with that extra time, hopefully, you come up with a wonderful idea of how to sell better in terms of new ideas and new products, not just repeating things that you’re doing all the time.

Damian: Right, well, on that, Happy New Year!

Stephen: Happy New Year! This is the year of the pig, I believe. It sounds prosperous, so happy and prosperous new year to everybody.

Damian: If you enjoyed today’s episode, we ask you to please leave a rating and write a review. Or, better yet, share with another marketer. Be sure to subscribe to the podcast for new episodes. Also, check out the show description for complete show notes and links to all resources covered in today’s episode. If you’d like to speak to someone about any topics covered in today’s episode, please visit buyergenomics.com and start a chat with the team today.

In this age of big data, data shortage is not a problem. In actuality, the opposite is the case. Retailers are frequently drowning in their own data, triggering an overwhelming sense of anxiety and disquietude.

Not only that, these companies typically do not have access to the resources than can transform this mess of data into something accessible, actionable, and profitable.

This first installment in our 7 Steps to Marketing Nirvana Series will outline how to eliminate data overload and transform your marketing team into data overlords.

Revitalize Your Customer Portfolio

The general reason most companies’ databases are inadequate, incongruous, and insufficient is that their Point of Sale (POS) systems were developed to solely ingest transactional data, which consists of five key categories:

Who bought? (Sometimes)

What product?

For how much?

When?

Through which channel?

While such information can be useful on its own, this is merely scratching the surface of what is truly possible. The fact is, these rudimentary systems have not been designed with the long-term relationship of a customer in mind.

For instance, POS systems do not track the trails leading from one purchase to another, and also do not attempt to understand the stimuli that incentivize each subsequent purchase. In addition, they are not directly actionable – rendering your data management abilities a far cry from their full potential.

Ingest/Filter Your Customer Transaction Information

Marketing software solutions have evolved dramatically over the past few years – offering small and medium sized businesses capabilities they have never had before. Predictive Marketing Automation (PMA) Software is one such class of software.

In contrast to POS solutions, this type of solution maintains customer databases, predicts the relative value of customers and automates marketing communications.

In turn, the first priority is to get your POS data organized, cleaned, and filtered for the sake of maximizing data hygiene. This is done by uploading all of the records into a PMA’s standard data model.

Think of it as cleaning a messy garage, fine-tuning a car in a body shop, filtering dirty water, assembling puzzle pieces together, or weeding a garden.

Keep in mind, one of the main goals of marketing is to know as much about your customer as possible.

Following ingestion through a standard data model, all of your retail information becomes stitched together across all available channels. This generates the omnichannel, 360-degree customer view that the mega-retailers strive for.

Without such an expansive customer viewpoint, it is impossible to accurately calculate anyone’s customer journey or respective Lifetime Value (LTV).

By simply uploading your customer transactional data into a PMA, you are already well on your way towards developing the comprehensive customer portraits, strategies, and services possessed by the Amazons of the world.

Make no mistake – without this level of comprehensive knowledge about your customer base (along with the ability to manage that knowledge effectively) your chances of succeeding – let alone competing – on a major league level are incredibly slim.

Define and Create Reference Tables

Reference tables are constructed from the various types of transaction codes used to track retail or online sales in POS systems. These typically include customer data, transaction data, and product data.

For example, if a buyer enters a furniture store and purchases a couch on clearance (where the price has been dramatically reduced), that particular item would have a special transaction code indicating the markdown in price.

This simple set of information can be used to form a clearer profile of each buyer. For instance, if the person who bought the couch also purchased other items on sale (either that same day or over a longer period of time) he/she could be categorized as a discount buyer.

Every nuance of each purchase (full-price, discount, coupon, time/date) that is captured by POS systems can be ingested into a PMA and built to form reference tables. Or, in other words, PMA’s ingest transactions codes and provide the magic “decoder ring” to make sense of the data.

Compile and Access Promotion History

One of the most valuable elements of a PMA is the establishment of a promotion history facility. This systematically tracks and deduces how each customer responds to various forms of communication and/or solicitation (email, messaging, offers, etc.).

The digital world we inhabit is a closed system in which any form of online activity can be readily tracked and stored.

Therefore, if your company sends out a promotional email to its customer base, a PMA can tell you what particular strides each recipient made towards a purchase. For instance:

Who opened the message?

Did they click through to the site?

What items did they browse?

Overall, how engaged is the customer with your brand?

Any of these bits of information can offer powerful clues about exactly where each of your customers are along their respective journeys and how to best manage the dialogue (or curriculum of communications (COC)) with them.

For instance, if a customer left an item in their cart, send a simple reminder within the next few days. This little nudge can be the final trigger towards making the actual purchase.

Marketing is all about relevance. Your chances of securing new customers or inciting repeat purchases from your existing base directly correlates to your ability to send the right message, to the right person, at the right time, via the right channel.

Define Your Metrics

Another component of this process is to define the most important set of metrics (also known as Key Performance Indicators (KPI)) that are specific to your business. These are ways of measuring your customer management and overall company performance.

Different cohorts of customers should always be treated differently. Some metrics can be more valuable than others in different contexts – depending upon the terms of the behaviors you are attempting to trigger in your customer base. Therefore, the most pertinent metrics should be defined and consistently evaluated.

Customer Acquisition Cost (CAC)

A crucial metric to evaluate in this context is Customer Acquisition Cost (CAC). CAC is determined by dividing all of the costs spent on obtaining new customers by the actual amount acquired in a particular time frame.

Ultimately, the goal is to minimize your CAC as much as possible by carefully assessing your Return on Investment (ROI) for each customer won.

Lifetime Value (LTV)

CAC should always be measured in tandem with another important metric – Customer Lifetime Value (LTV). This determines how much a customer is worth to you over their lifetime.

Customer value metrics can also be assessed in relation to a number of different factors and time frames (i.e. 1-year, 2-year, historical, etc.)

Historical Value

A simple metric is to examine how much a how much money a customer has spent in the past over various time frames.

For instance, say that a frequent flier has flown 1 million miles with an airline over his lifetime, but now no longer flies as much as he used to. Meanwhile, another customer has flown less overall (say 500,000) but much more within the past year.

Despite the fact that the former has flown with the airline twice as much overall, he would likely not receive the same degree of perks as the the latter. This is because the frequency of recent miles outweighs the overall number of miles over a longer period of time.

Projected Future Value

Projected future value (or potential value) is a key metric that is often overlooked. If you blindly assume that every customer has the same potential value, then you are likely missing out on a range of opportunities.

You should always aim to recognize customers for what they’ve done in the past while also incentivizing future behaviors leading to more subsequent purchases.

For instance, if you do not know what a target or cohort’s potential is, how would you be able to truly define your level of marketing success? Your company should have the ability to answer a few key questions:

Are we achieving the full potential of each type of customer that we have in our database?

How much should we invest in each customer in the future? Via which channel(s)?

The bottom line is that a PMA system can evaluate both historical and current customer behavior to calculate the respective value of any customer down the road. This is an easy, accessible way for marketers to justify marketing expenditures by using the software to show their finance departments exactly how investments in particular omnichannel campaigns are delivering the required ROI.

Category Value

Category value is a calculation of how much and how frequently a customer spends on your particular product or service in proportion to others..

While many marketers would love to have a clear, accurate view of category value, they often lack the resources and technology to do so.

Let’s go back to the airline example. Say a customer flies a total of 100,000 miles per year, but only travels 25,000 with your airline. A PMA can dive into survey data, cluster/cohort analysis, and gaps between activity and explain why that particular customer is spending their money elsewhere.

With this beneficial knowledge at your fingertips, you can tailor your interactions and offers to that customer in order to win back a greater percentage of their buying potential while gaining an edge on your competition.

Conclusion

By this point, your customer and transactional data have been uploaded into PMA software, where it has been systematically organized and filtered.

Not only do you now have a clean garage, you also have a well-oiled, fully fueled vehicle ready to hit the road on your path to Marketing Nirvana. But in order to get there, you need a roadmap identifying who your customer base is, and where to find them.

Gary’s background includes over 30 years of analytics & database innovation for several leading Fortune 500 companies and Madison Avenue advertising agencies. Gary has been a frequent lecturer and author on the topics of database marketing and applied statistics. His articles have been published in DM News, Direct Marketing and the Journal of Direct Marketing. He recently was President of the Direct Marketing Idea Exchange and served on their Board. Gary received his M.S. in Industrial Administration from Carnegie Mellon University.

TRUSTING YOUR MARKETING RESULTS:
CAUSATION VS CORRELATION

Summary:

Correlation can be an amazing tool to discover causation, but sometimes it’s just too expensive or not worthwhile to even go that far. If the correlation works and you test into it, that doesn’t mean you break out an extra million bucks. You test into it and if it holds up and it’s true over time then make money with it. Don’t worry about it. Go solve another problem.

Below is a lightly edited transcript of Episode 32 of the Inevitable Success Podcast with Damian and special guest Stephen Yu. (Listen Here)

Transcript:

Damian: Google is literally saving lives. Are they? Maybe, maybe not. So, in a recent study that we had found since 2006 to 2011 the murder rate in the United States has dropped every single year a near-perfect correlation with people shifting away from Internet Explorer and Edge to Google Chrome. So is Google actually improving the safety of the Americans? Or is this correlation versus causation?

Stephen: The short answer is we don’t know. Maybe, maybe not. And if you took any economics classes in college they say, “Yeah every time there’s a war, the U.S. economy grows.” So war is good for the U.S.? Well if you just look at it from an economic stance it’s not war. Is the war the cause of all this? Maybe, but we are not here to have a philosophical discussion about causality vs. correlation. We’re here to say that marketers, especially when you’re dealing with a lot of data, we see interesting correlations all time but do we jump to conclusions or do we take a step back and say, that sounds interesting but do we act on it? I guess the long and short of it is, no just act on it if the coalition is really, really strong and if it makes sense, not all the way digging back to causality.

Damian: If we kind of go back to the Google example, I think it’s cute and it’s funny. It’s most certainly not true that it’s causing it. It’s certainly true that it is correlated though, and I think in today’s world as everything that turns into data and there are more data sets that are easy to compare to each other, you’re going to find more and more correlations. So I think the point that you’re making is that sometimes these correlations can tell you stuff that is actionable and can make you money, and sometimes you can be wrong on the causation and it can still work and I think that’s what we’re talking about.

Stephen: That’s what I’m trying to say. And also in the predictive business, we talked about predictive analytics some time ago, let’s bring back what it does and doesn’t do. Well actually they do a lot of things, but there are easier things to predict and harder things to predict. For example, predicting who’s going to do something. The who part, yeah that’s really established. Do you want to sell something? Who’s going to buy what, we know how to do that pretty, pretty well. So who means – okay who is more likely to go on a luxury cruise? Okay. With all the demographic data in past behavior can predict that. If you flip that and say that this person is coming to the store all the time, what is he going to buy next? We can do that too. How do you think that all the collaborative filtering happens on Amazon – if you buy something – oh he must be interested in that too. Well, they’re predicting what you’re going to buy the next. The second hardest thing is when. Okay, fine you’ve predicted that somebody is into luxury goods. Will, she buy some really expensive Italian handbag this Christmas? Now that’s hard because now you have some other type of empirical data to know exactly when. This is why in the marketing world what we call hotline names is so important. Or anything, like for example, I just moved by the way, and I must have left a lot of trails. In fact, it was a little spooky because I said something about moving in Facebook and before you know, it all the Street Easy ads are starting out on my wall.

Damian: Right.

Stephen: So I said, “Well this is interesting, they must be listening to everything that I say now. It’s OK because I kind of bought into it and this is what I do for a living too, so I got to say you know it’s okay.” You know? But it’s still innocuous. The point is we know how to do these things, we know how to read, so even the when part is not impossible. Yeah, this guy’s giving user data. In fact, there’s no model, there’s no predictiveness, they just responded to what I said. Now, what is the hardest thing to predict? It’s why? Why do people do things? We don’t know that.

Damian: Well actually, I wanted to see – we were talking a little bit earlier and I know you have an example from your past client experience where the correlation was very profitable.

Stephen: Oh it happens too.

Damian: Yeah? And I have an example from my past experience where the correlation was very unprofitable. So let’s, I think we can go through both. Why don’t you jump off, I think it there was the septic tank example.

Stephen: Oh septic tank yes, this happened in real life. We were helping out a luxury furniture catalog and online store. And we were building models to find out, again let’s talk about who. Who is more likely to buy furniture through a catalog.

Damian: Right.

Stephen: This is not cheap furniture by the way.

Damian: Okay so premium catalog for furniture, okay.

Stephen: And then they’re building models with all kinds of data, all kinds of behavioral data, behavioral meaning that he something similar in other places that type of thing, and the demographic helpers, also income, what’s the gender, head of household, age, all that stuff. And then all of a sudden this census-level data popped up and it was a percentage of septic tanks in a neighborhood, popped up in a model as a very strong variable. And by the way even when something is really highly correlated, we don’t use just one variable, that’s not even a model, that’s more like your gut feeling. But we don’t do that. But that popped out and we all scratched our heads. What does this mean? So again, is this causality? If you have a septic tank you do this? And then we realized that no, it’s telling us something. We’ve got to trace back, trace back to see if it makes sense.

Damian: It certainly was correlated though.

Stephen: It was strongly correlated. So we said, okay so let’s just say that what people have a septic tank? Well their house would be a bit large right to have it, and the town should be pretty far away from the city center to have a septic tank, you don’t even have a sewer system connected to the house? It is telling us something and what we said was, yeah it is a weird variable. We would have never picked it without math on our own, there’s no way. But it’s telling us something and let’s use it. So we used it, and it worked, because it was telling us all those things that I said here: certain size of a house, certain type of a household, single family unit, pretty far away from city center, certain income level, were all correlated to this particular furniture catalog. So we said, I don’t know why – again I stop asking the why part, but let’s use it and it worked. So I’d like to hear your story about when it did not work.

Damian: Sure. So one of the things about that, that when we were talking about it, it’s okay, it gave you a hint to something that you could wrap your head around well why could it – you start using a computer in your head to figure out why, why that would occur.

Stephen: Sort of human function actually.

Damian: Yeah.

Stephen: By the way all the machine based models, they just do it. They don’t really reason as humans do. Funny thing about it is that when you have a lot of variables, the machine will find substitutes anywhere.

Damian: Yeah, but I mean I think there are situations where that correlation could break down into unprofitability. So for example, it’s very rare but maybe there’s a growing city that still doesn’t have their sewer system yet, and you live one block away from, you know, a place that you can walk in and buy furniture, that correlation will break down from profitability because the premise, the cause was that they still had a beautiful house but they were just too far away to get in the car and go drive.

Stephen: That’s why you should never use just one variable.

Damian: Right.

Stephen: This was one of like 10-12 variables in that model. So it’s never the one thing. So that’s another thing that I want to point out is that when we say build a model, by the way, even machinists when they build a model they never use one variable. In fact, we use about 10 variables, if the one variable is really, really obscenely too strong and it takes up like 80-90 percent of predictability power, we throw that out, because if that one variable doesn’t work then you’re really screwed later. So modelers, mathematicians, they’re all about hedging bets and what is a regression model? Regression is nothing but a curve that has the least amount of error rate on the average. The curve that is the least wrong. That’s the regression curve. So yeah we don’t want to hedge all our money in one variable, we don’t have it –

Damian: Correct.

Stephen: Yeah that’s a big caveat that I want people to remember.

Damian: So the story that I have was, I don’t know, maybe this could have been 5-7 years ago or whatever, but I remember I was looking at Google Analytics accounts for some e-commerce websites and I even remember like, especially earlier in my career you’d read articles that say you know, like it was hard to track things back then. So page views were like a really easy thing to track because everyone had access to it. And there was this like running theme in marketing forums and vice versa, all those places that if you could increase the number of page views in your sessions, then those were more engaged and they had higher conversion rates. And I remember digging deeper and deeper, deeper into it and I was kind of buying into it because I was looking at all these different accounts, and I saw that yeah that’s true. Like the pages that the sessions that have all these high engagements judged by that metric were extremely correlated to very, very high conversion rates. And then I looked just a little bit deeper and I realized that wow, all of these websites have multi-page checkout steps. So by definition, if you went to check out you increased your page views by 5.

Stephen: Oh right.

Damian: So if you, in hindsight like you couldn’t buy unless you had that many page views, therefore like was it really describing a good session that was engaged or were those the people that you know, you had to have that many pages to check out? And then it kind of started this whole other process where, is a landing page really great if you were, or a website really great if you have to go to so many many pages to check out? And then it was like well actually maybe the best sessions and this actually proved to be true, the best converting sessions were the ones where somebody landing on the landing page went straight to check out. There was no navigating or shopping, it was buying. And that actually is one where if you bought into, I should encourage people to keep having more page views, it was wrong. It actually hurt, it was the inverse. And I was just I guess that’s my story.

Stephen: That is a very good example. And this is why what you just did here is exactly why humans will have someplace even in the machine-driven world, is we reason. The second point is that the reason we have to dig deeper into not just pure data, but you have to even think about how the data is collected. And I have a similar example when I was at a data vendor really or a compiler, and we had no shortage of data, and we were building a model for a certain client and we found out that certain regions, by the way, when you’re in a compiler business you know that in certain states it’s hard to collect certain types of data. So when that data popped in –

Damian: What do you mean? Give me an example.

Stephen: In other words, when you compile the data, you don’t know everybody’s home value by the way. So a lot of things are outsourced and somebody actually sometimes stands in line in the local city government and finds out what all the house prices are. Well, they can troll the web, but the point is there are some variables that are collected that way. The point that I’m making is that certain variables if you know the history of it, you have to tell the difference between actual consumerist behavior or some loophole in the way we collect the data. So you’ve got to really think about not just what you see in front of you, oh yeah it looks like it’s highly correlated. And that’s what you just did, think why so many page views? Because the website is poorly designed. In my case it was more no, no, no in certain states it is hard to collect such and such data, and if that’s popping up so prominently.

So you know what, let’s look at this, compare this with a store footprint because you cannot argue that if you have a lot of store footprint you have more concentration of people in those states right? And it was almost an identical match, so that variable should be thrown out. This is why, again going back to the point number one, humans still have a place to reason and make sense of all this, but that does not mean that the analysts who do these things should have an endless pursuit of oh I want to know why. Because the why part, and this is why the why part is the last and hard to predict. Sometimes you just have to ask why. We talked about three types of data, about a few episodes ago. You have behavioral data, demographic data, and attitudinal data. Attitudinal data is scarce because you have to actually stop and ask questions in the form of primary research, or survey, or even social media listening. But it’s impossible to listen to everybody and it’s impossible to know everybody who answered it either. It is really hard to marry such data on a personal level with all the other behavioral and demographic data. In the pursuit of why that’s what you need to do. So, I’m not saying that asking why is not important, even when you see a variable you know, in a really well-built model you have to pursue to find out, okay what’s the background of all this data? Does it make any sense? Why are a septic tank and all those things showing up in my model? Yes, you have to think about it. That doesn’t mean that you have to stop and pursue the why so hard that you have to start primary research.

Damian: Right.

Stephen: Sometimes you just have to act on it.

Damian: I think the essence of what I take away from you’re saying is, one, correlation can be an amazing tool to discover causation, and two sometimes it’s just too expensive or not worthwhile to even go that far. If the correlation works and you test into it, that doesn’t mean you break out an extra million bucks. You test into it and if it holds up and it’s true over time then make money with it. Don’t worry about it. You know, go solve another problem.

Stephen: That’s right. And I’m trying to communicate the price of prediction. There are a lot of marketers ask that question first. Now the marketing is a part of the product planning stage and I have met such people in Korea actually, there is an amazing company that does all that social media listening, and they were helping companies like LG, Samsung, and all those companies and they actually figured it out by listening to the social media comments that some company made a very small washer and dryer set, thinking that yes single people might buy this thing. The assumption, great in all scientific research –

Damian: I know this story. You’ve told me this story, it’s a good one.

Stephen: Yeah. So and then they realized, wait for a second, we made this thing – they don’t buy them.

Damian: So single people didn’t buy the smaller washer and dryer.

Stephen: Because you know why? They’re too busy socializing, basically they follow tweets that they make you know? They want to have a big washer and just have one load once in a while. The lifestyle makes sense.

Damian: Basically they don’t want to do laundry all the time so they’re like I’m going to let this laundry pile up in a corner and then I’ll do it all at once.

Stephen: That’s exactly right.

Damian: And I don’t want to spend all day doing it. I want to do it one time.

Stephen: In fact, my wife who washes quite frequently, doesn’t even need, because she washes so frequently that she doesn’t even matter for her that much. The moral of the story is this, the company spent a lot of money doing this because they were actually planning a new product. You don’t want to build a wrong product to have to listen and ask, do the survey and do the panel research, you’ve got to do all these things right? But when you are in a one to one marketing mode, let’s not go crazy. Sometimes you find a good correlation, count your blessings and act on it, if it doesn’t work, go to Plan B.

Damian: I think that’s a great place to end. And you know in the meantime, if you’re going to use Internet Explorer versus something else, make sure that you do it in the winter because we also found that ice cream sales are extremely correlated with murder rates. So there are lower murder rates in the winter. So that should cancel out your risk of using Internet Explorer.

Stephen: Stay safe.

Damian: Stay safe people. Take care.

Damian: If you enjoy today’s episode we ask that you please leave a rating and write a review. Or better yet share it with another marketer. Be sure to subscribe to the podcast for new episodes. Also, check out the show description for complete show notes and links to all resources covered in today’s episode. If you’d like to speak to someone about any topics covered in today’s episode please visit BuyerGenomics.com and start a chat with the BG team today.

USING PREDICTIVE ANALYTICS FOR MARKETING
THE FUTURE OF MARKETING AUTOMATION

What is Predictive Analytics?

Predictive analytics is the utilization of data, statistical models and machine learning capabilities in order to pinpoint the probability of future results based on historical, demographic, and other behavioral data.

Through careful examination of what has happened in the past, you can determine the greatest likelihood of what outcomes to expect down the road – and act accordingly. This maximizes the power of your data assets and puts your business in the best position to succeed.

This guide will describe the benefits of predictive analytics (when properly utilized), and show why its role in marketing is important. We will also break down the collaborative connection between analytics and automation, and offer insight on how artificial intelligence (AI) will continue to evolve these techniques in the years to come.

What is the Predictive Process?

Predictive Analytics has been a cornerstone of data-driven marketing, and it is gaining momentum as the size and variety of data keep increasing. Not only does it consolidate and simplify data, it makes it useful by charting a course of action for obtaining and retaining customers to maximize profits. Simply, predictive analytics provides answers to questions.

However, in order to be truly effective, the framework must be designed, and algorithms must be developed and deployed properly. If not, the consequences can be time-consuming and costly – devouring precious resources and damaging hard-earned credibility.

Obtaining these answers is the chief goal of predictive analytics in marketing. But exactly how is this done?

Filling Blank Spaces in Big Data

We live in an age with an immeasurable amount of data, where every single step along the customer journey is tracked across multiple channels and stored in massive databases.

With the Big Data movement in full swing, there are plenty of holes inhibiting the most accurate answers to the key questions listed above. However, proper data strategies, predictive techniques, and technologies can color in these blank spaces in order to make the marketing landscape more vivid and readable.

While data collection is more thorough and comprehensive than ever, it is still impossible to know every single thing about every person – especially regarding their future behavior. In fact, the sheer magnitude, diversity, and speed of big data have generated a range of obstacles inhibiting its efficient consolidation and implementation.

These days, in order for predictive analytics to be the most effective, carefully designed data collection and refinement steps are key. By obtaining multiple sources of data from the widest variety of consumer channels (both online and offline) and building a 360-degree customer view around each target audience, predictive analytics opens doors to areas previously invisible to marketers.

Predictive Modeling

That is precisely why predictive models are built – to fill in these big data gaps through data mining in order to obtain a cohesive, digestible, and actionable future outlook regarding buyer behavior. These can be used to model your best customer behavior and suggest which actions to take on a personal level. Some applicable modeling techniques include customer segmentation, customer lifetime value models (CLTV), product affinity models, response models, churn prevention models, etc.

Keep in mind that – as with many aspects of life – there are no truly definitive answers in predictive analytics. Conceptually, modeling is about making the most out of what you have available – not about creating flawless datasets.

Model Scores

One critical function of a predictive model is to summarize and condense convoluted, seemingly disparate data points into simple, easy-to-read “scores.”

A basic, yet exemplary predictive modeling method involves generating a personalized likelihood score for each buyer – for example, on a scale of 1 to 10. In this case, the higher the score, the more likely the chance that person will engage with your business, gravitate to your products, respond to offers, and make a purchase via a specific channel (depending on the nature of the models).

For instance, all retailers deal with loyal and valuable customers as well as one-and-done bargain seekers. The latter are undesirables. Without a doubt, attracting the most loyal and valuablebuyers(Most Valuable Buyers, or MVBs) who not only come back but continue tobuy again over a long period – is the key factor of true retail success.

So how can retailers find MVBs in such a large pool of prospects? It can seem like finding Waldo in a crowded picture.

Determine Your Target

The answer is to “target” such potential MVBs via loyalty or value models that can “mimic” their behaviors.

The following is a breakdown of the high-level steps for developing MVB models for the purpose of prospecting.

First, we must determine what “value” means in specific terms. Value could mean sheer dollar amount (either lifetime or the past 12 months), frequency of purchase within a set time period, longevity of a relationship, time elapsed since the last purchase, and – more importantly – combinations of all of these factors. To be effective, the target should be neither too large or too small. To start, you can focus on the top 15-20% of customers in terms of lifetime spending level, more than two past purchases, and the last transaction within the past 12 months. Such factors depend heavily on your respective business model.

Decide what the comparison universe (the opposite of the target) should be, since a model is an algorithmic expression of “differences” between two dichotomous groups. In other words, the non-target is as important as the target universe.

If finding potential MVB in the “customer base” is the goal, a trained analyst should examine all available historical transaction, promotion and response data, demographic data, and other behavioral data (such as web browsing data) to identify what factors differentiate potential MVBs from all others. For prospecting purposes, mostly non-transaction data would be used (since purchasing behavior wouldn’t be available).

Once you have the algorithm that would “score” potential MVBs, apply it to the general target universe, so that the user can easily target “high” score individuals.

These are just high-level steps, but the key takeaway here is that defining both the target and the comparison universe is the most critical part of all. To accomplish this, marketers must be on the same page as the analysts who actually develop the models.

In other words, the users must be clear about what they want out of the models, such as longevity of a relationship, high revenue (e.g., high average amount, multiple transactions per year, etc.).

Applying Your Scores

Model algorithms are generally developed with samples. To be useful, final models must be applied to the pool of names used for marketing campaigns.

Once the resultant model scores are available to users, decision makers can easily identify the potentially high-value customers by gauging scores for MVB models (the higher the score, the better). They can then focus on guiding them towards becoming full-scale MVBs through personalized, specialized treatment. On the other hand, the low scorers indicate would-be priority customers who are showing relatively low potential, leading to 1-time purchase.

In another instance, a Loyalty Score would differentiate customers with long-term potential from customers who are showing signs of attrition or are already becoming dormant.

In the latter instance, a series of proactive, targeted, individualized messages could be sent autonomously (see below) in order to prevent churn and “rescue” these fading customers.

An example would be a set of properly spaced, carefully worded emails highlighting “exclusive” discounts designed to get those priority customers re-engaged with your brand and back into the fold.

Winning back potentially high-value customers at this point is crucial. If you lose a customer, the odds of him/her ever returning drop drastically. This results in a series of wasted opportunities..

Without a properly defined, targeted, and coordinated predictive modeling mechanism, you cannot know which customers to prioritize in your marketing campaign. Consequently, precious time and resources would be wasted sending the wrong messages to the wrong people.

Marketing Automation – Making Analytics Actionable

A marketing automation platform allows marketers to synchronize, streamline, and execute their omnichannel marketing campaigns from a single, centralized apparatus.

It is not enough to simply identify and understand the browsing/buying habits of your current and potential customers. In order to initiate a sale, that knowledge must be put into action.

Proper implementation of targeting and messaging will allow you to reach the right customer, at the right time, on the right channel, with the right message. In many cases, this type of advanced personalization can make all the difference between winning or losing a customer.

Aspiration, Adaptation, and Application

Let’s say you’ve put in the work. You’ve asked the right questions, defined your goal, extracted and assembled your data, built your model, and got your answers. Now you want to use that information to grow and nurture your sales and customer base through a targeted marketing campaign.

Keep in mind that customers are becoming more fickle, spontaneous, and impatient than ever. With such a wide variety of consumer channels at their fingertips (web, social, email, mobile, tablets, and other addressable media), their interactions with brands can seem scattershot and haphazard.

Meanwhile, every single message you send should be individually targeted towards a specific buyer who can be at any given point along the customer journey at any time.

Sending such a timely, targeted, personalized message/offer designed to acquire or retain a buyer requires speed, proficiency, and accuracy. Thanks to advances in predictive analytics – where even modeling development can be automated via Machine Learning and AI – the whole process discussed above can lead to success in less time and (with fewer resources) than the past.

Know and Grow Your Buyers [To Their Full Potential]

We’ve reached the point where efficiently measuring and responding to consumer behavior across such a wide array of available channels is beyond the realm of simple legacy Email Service Providers (ESPs).

In this elaborate, multi-dimensional landscape, marketing automation tools can implement algorithms to hone in on your target audiences and proactively adapt to their behavior. As the software ingests more data over time, the system begins to recognize patterns and enhance its recommendations on its own. This radically simplifies and streamlines each customer interaction while simultaneously maximizing business potential.

21% plan to implement a new marketing automation platform in the year ahead

82% of marketers recognized a positive return on investment (ROI) from marketing automation and said it makes them more efficient.

With the right combination of predictive analytics and marketing automation software, you will continuously keep your brand ahead of the curve – and on the minds of your customers.

The Future of Predictive Analytics and AI

The scope and efficiency of AI’s capabilities have been growing rapidly and will continue to do so in the years to come. In fact, the amount of funds spent on AI worldwide is predicted to climb to almost $40 billion by 2025.

Meanwhile, predictive marketing platforms are becoming increasingly automated, with less human involvement necessary than ever. Does this mean that humans will eventually be able to just sit back and let the machines do all of the work for them?

In addition, machines will not be able to completely understand why humans want specific tasks done, which is a critical component of any marketing campaign.

Basically, machines will continue to increase the speed, scope, and precision of both predictive analytics and marketing automation in the future, but they will still require logical, perceptive humans at the helm to chart a course of action and deliver instructions. After all, automation is really about executing what humans know how to do already.

Conclusion

Equipped with this valuable information about each individual buyer’s interests, inclinations, and position along the customer journey, predictive marketing platforms can autonomously initiate carefully targeted, personalized conversations across any available channel at any time.

This is the age of one-to-one marketing, where reaching buyers on a personal level via the proper channel is becoming more essential than ever. In a field where every competitor is perpetually striving for relevancy,a predictive marketing automation system is vital to success.

At BuyerGenomics, a well educated client is our best customer, and we’ve poured our experience and resources into this piece to that end. If you picked up even one or two new insights, you’ve succeeded today.

If you’re interested in learning more about the BuyerGenomics Predictive Marketing Automation Softwarehere.

CUSTOMER DATA PLATFORM: [THE MARKETER’S GUIDE]

What is a Customer Data Platform (CDP)?

If you’ve heard the term “CDP” but don’t know just what it is – you are not alone.

The term “CDP” has generated considerable coverage in modern marketing over the past couple of years, but it has never been more relevant than right now. Essentially, a Customer Data Platform software is a web-based interface that consists of three core components – a database, the ability to connect to multiple channels, and a marketer-friendly interface – that help drive marketing and sales initiatives.

What You Will Learn Here

There are other platforms that contain some of these capabilities, but not all (and not to the same degree). These include CRM (Customer Relationship Management), DMP (Data Management Platform), and Data Lakes/Warehouses – which we will break down a bit later on.

That is why nailing the true definition can be so confusing. However, with over 20 years of innovation in marketing, analytics, and technologyunder our belt (plus access to a vast network of experts) we not only know what a CDP really is, we’ve figured out what sets the best Customer Data Platform apart from the rest.

With so much disparate information out there, we sought to do more than simply define a CDP and its capabilities. Plenty of other marketing companies and media outlets have already attempted to do that (with varying degrees of success).

Instead, we want you to know exactly what a CDP can do to substantially enhance your company’s customer base, form (and build) legitimate, lasting consumer relationships, and increase profits.

After we break down exactly what a CDP is and what distinguishes it from other platforms, we’ll move on to how you can (and why you should) utilize this highly advanced – yet incredibly practical and accessible – technology to not just attract new customers, but identify and retain the most valuable ones that drive your business.

What a CDP is [and What it Does]

Make no mistake. There is no shortage of customer data. The true problem is that much of it is disorganized and incomplete. As a result, businesses struggle with ways to efficiently sort through it all and consolidate it into something useful, actionable, targeted, and personalized..

Customers tend to be fickle, spontaneous, and impatient. Today, customers simply do not interact with brands in a linear fashion. Instead, they come into contact with numerous ones across a multitude of channels (web, social, email, mobile, tablets, and other addressable media) at any given time – generating a multitude of data points along the way.

These days, a firm grasp of the Customer Experience (CX) is viewed as a critical way to stand out from competitors. CDP software utilizes an intelligence model that sifts through these scores of fragmented data, extracts what is relevant, and uses it to form unique, up-to-date customer profiles, build relationships, via data-based personalization, and ultimately increase sales.

Gartner defines a CDP as a marketer-friendly, web-based interface that integrates four core capabilities:

In addition, a CDP can be piggybacked onto any pre-existing system mentioned above, in a specialized manner that unique to the client and its needs. Other functions include analytics, reporting, tracking, and BI (Business Intelligence). With such a versatile set of tools, the unpredictable becomes legible.

CDP vs Other Platforms

People often ask about the differences between a CDP and other marketing platforms. The most prominent queries are how a CDP compares to a CRM (Customer Relationship Management), a DMP (Data Management Platform), and Data Warehouses and Lakes. This can create some confusion, especially since they all seem relatively similar on the surface.

In reality, there are a number of clear-cut distinctions that differentiate a CDP and make it stand out from the rest. Other systems – like the ones listed above – were designed with comparable goals in mind, but with differing (and generally fewer) functions, scopes, and capabilities.

CDP vs CRM (Customer Relationship Management)

While a CRM works to connect with consumers and utilize data to form customer profiles (like a CDP) – they simply are not designed to filter enormous quantities of data from so many sources. They also limit the amount of detail of ingested data, lack advanced identity matching capabilities, and restrict outside access to their internal databases.

Essentially, CRMs work well for targeted tasks within specific channels, but lack the depth and versatility to fully manage modern customer data like a CDP.

For DMPs, these pre-built audiences are used to enhance targeted display ads. On the other hand, a CDP uses predictive analytics to discern patterns, simplify data, and put it to use.

DMP data is also cookie-based, which means that a typical DMP profile only lasts for about 90 days before terminating. A CDP employs persistent, finely detailed, real-time customer profiles that last indefinitely.

CDP vs Data Lake/Warehouse

Data Lakes and Data Warehouses are specially-developed IT endeavors that typically cost more time and money to install than a CDP. Oftentimes, data warehouses are updated at designated periods, unlike CDPs – in which data ingestion is an ongoing process.

Also, since data warehouses are designed and run by IT teams, marketers have to frequently depend on the IT Department, which slows down the whole process. While technical participation and know-how is still necessary with a CDP, it is much more accessible overall. Therefore, marketers can directly access and run a CDP much more smoothly and efficiently.

Classifying Your Customers [With a CDP]

In Gartner’s 2017-2018 CMO Survey, marketing leaders said they invested two-thirds of their budget in supporting customer retention and growth through digital marketing.

One of the reasons why this is so important is because there are different types of customers (i.e. gender, income, interests) who are always at different stages of purchasing and skipping across multiple platforms. By identifying who these customers are (through response, engagement, and conversion data) understanding their tendencies, and tracking their buyer lifecycle, a CDP increases Customer Lifetime Value (CLTV) and transforms variables into certainties.

Strike While the Iron is Hot

It isn’t enough to simply understand the browsing and buying habits of current and potential customers. For many, in order to initiate a sale, you have to reach the right customer, at the right place, at the right time.

We’ve come to the point where efficiently measuring consumer behavior across such a wide array of available channels simultaneously is beyond the realm of human capability.

That’s where a CDP comes into play. It utilizes the most advanced forms of artificial intelligence (AI) and machine learning to sift through and unify an incredibly extensive array of data across all channels and devices not just quickly – but autonomously through predictive analytics.

Applicable statistical techniques include logistic regression, advanced data mining techniques, and neural networks. These can be used to model your best customer behavior and suggest which actions to take on a personal level. As the software ingests more data over time, the system continues to learn on its own and enhance its recommendations.

With a centralized, granular,complete, 360-degree customer view now in place, a CDP can create a feedback loop that develops a specific, singular customer profile. From there, it calculates buyer potential and takes proactive action to maximize purchase probability through automated decision-making.

The One-Time Buyer

With the incredibly high bar set by mega-retailers like Amazon, Walmart, and Target, many retailers are struggling and in dire need of an innovative, effective way to form personalized and lasting relationships with their customers. Additionally, subscription-based platforms – from Netflix to Dollar Shave Club – have experienced a meteoric rise by essentially guaranteeing year-round customer retention.

This leads to a critical conundrum: Can traditional retail/e-commerce companies find ways to not just attract new buyers (which is already incredibly costly), but retain them in a manner similar to modern subscription businesses?

With so many purchasing options out there that can be accomplished with a simple swipe of a finger or the click of a button, consumers are – by and large –extremely fickle.

The truth is, most retail buyers do not come back after their first purchase – about 75 percent to be exact. This means that most of a retailer’s customer base contributes little to negative profit. To put it lightly, this is not a recipe for sustained success – especially since itcosts so muchto attract most of these one-time buyers in the first place.

The MVB (Most Valuable Buyer)

On the other end of the spectrum is the MVB. These customers, while only making up about 15-20% of a company’s total customer base, typically account for more than three-quarters of all revenue.

These are the loyal consumers who are willing to spend more money, more often than the rest. Obtaining a one-time buyer is basically equivalent to gaining a subscriber, except they are not spending a fixed amount over a structured period of time – like $9.99 per month. In fact, there is nothing holding them back from spending as much as they want, as many times as they choose.

Great businesses are built on great customers, and MVBs form the backbone of any successful business. With an all-encompassing, 360-degree, multi-channel view of consumer behavior,a CDP can help youfind prospective MVBs, develop them accordingly, and continue to identify more along the way.

93% anticipated that employment and analysis of customer data in decisions and campaigns would create a noticeable shift in their ability to meet disruptive and competitive challenges.

53% said that the transparency provided through CDP platforms enabled their teams to react more quickly to changes in markets or customer preferences.

Inactive/Fading Customers

A CDP can prevent the dreaded churn by automatically identifying shifts in their status across and strategically reaching out to fading customers before they fall into attrition. One way this can be done is by employing ESP (Email Service Provider) functions to send a special offer for your product or service at a premeditated moment. This act of “rescuing” fading or inactive customers is extremely critical – because once you lose a buyer, you’ll have to fight twice as hard to win them back.

A CDP’s RFM (Recency, Frequency, Monetary Value) scoring engine classifies customers and sends the right follow-up message at the right time. The best CDP’s go far beyond RFM, and calculate near-time or real-time model scores, for every customer, predicting their likelihood of purchase or attrition.

Real Life Customer Journeys [The “Customer” in CDP]

Within each moment is an opportunity. When those moments are captured and managed in an intelligent, methodical fashion, a CDP enables you to transform transactions into relationships.

We spoke with a few model consumers who fall into the MVB category and were willing to share why.

Sneakerhead

The first is a man in his mid-20’s who is an avid sneaker collector (“sneakerhead’). For him, footwear goes far beyond mere practicality. He was an avid basketball fan as a child, which branched out into a heavy interest in sneakers over time.

In high school, he always made sure he had two or three solid pairs to choose from every morning. At the time, that was all he could afford.

But once he entered the workforce and began earning a respectable income, the scope and price of his purchases increased drastically. Eventually, he developed an entire closet just for his sneakers – many of which were rarely used – if at all.

“To me, it’s a lifestyle,” he said. “It’s part of my identity, and I feel like I’m a member of a community. It’s a culture.”

Fashionista

The second is a woman in her late-20s who considers her closet to be the centerpiece of her upscale city apartment. It is a sizeable walk-in filled with a wide variety of high-end designer outfits and accessories – each for different occasions.

While she always had good fashion sense and liked to dress well, her fascination with fashion truly took root while shopping for business outfits at her first job. She bought what she could afford at the time, but looked forward to when she could shop freely without limitations.

After a hard-earned big promotion, she started branching out and expanding her horizons. She followed all of the latest trends, and developed an impressive collection of dresses, tops, pants, jackets, handbags, and footwear.

“Fashion is more than just a hobby,” she said. “What I wear affects how I carry myself and how I’m viewed by others. I’m on display everywhere I go.

“It literally applies to any situation,” she added. “Whether I’m at work, out with my girlfriends, relaxing at home, or dressed up for a special occasion. When I shop, I’m rewarding myself and celebrating who I am.”

Key Takeaway

Equipped with a CDP, any retailer could easily identify either subject as a current or potential MVB. From there, they could monitor purchases and channel activity, gauge/influence brand loyalty, and deliver targeted, timely updates on the latest releases and promotions in order to actively increase the likeliness of another transaction.

For instance, if either consumer regularly demonstrates an affinity for one particular style or category of a product, they can actively respond by offering corresponding product suggestions for the next purchase.

An elite CDP helps to paint accurate, intricate portraits of your buyers. By knowing exactly who you are selling to, you are ahead of the game. In turn, the odds of securing and/or retaining MVBs skyrocket.

57% felt hindered in their ability to carry out broad digital information.

22% felt their data and analytics capabilities were lacking.

13% were unable to improve targeting and personalization.

10% endeavored to build better marketing automation.

9% wished to better understand their customers’ journey.

Clearly, properly grasping and implementing customer data is a considered a key problem in the field. However, a CDP is the most practical under a particular set of circumstances.

One factor to take into consideration is cost. CDPs employ a vast set of sophisticated, cutting-edge technologies. This requires investing a substantial amount of time and money. Generally, most CDPs fall in the realm of six figures per year.

In addition, your company should be large enough and possesses the standard customer-facing systems and staff to properly utilize/analyze the technology. While it is a powerful tool, a CDP’s true effectiveness depends upon how it is designed, implemented, and managed.

However, if employed the right way, the benefits can easily outweigh the costs. A CDP lessens the amount of time and money necessary for gathering, filtering, and activating data while actually increasing its utility. In fact – with the best software – the resulting spikes in sales can pay for the for product multiple times over.

Conclusion

Simply put, a CDP’s function is to collect, cleanse, organize consumer data, and transform it to become highly actionable. In fact, it can process any kind or class of data currently available without limit. But it is more than just a multi-channel database and interface – it has become an interactive and innovative analytics and intelligence model.

With a clear, coherent customer data strategy, the right implementation tools, and a proper cross-functional team in place, a CDP can hasten, strengthen, and broaden your organization’s marketing framework.

Businesses do not just want to collect, assemble, and assess data. They want to apply it into something useful and generate concrete results.They want to grow their customer base, develop real, personalized, lasting relationships with their buyers (MVBs), and maximize revenue.

At BuyerGenomics, a well-educated client is our best customer, and we’ve poured our experience and resources into this piece to that end. If you picked up even one or two new insights, you’ve succeeded today. Not everyone completes reading an entire work of this scope. If you did, you’ve done what most readers do not, and distinguished yourself in the process. Congratulations — knowledge is power.

You can accomplish all the goals – and more than you would expect – from a CDP effectively, intuitively, and swiftly.

A/B TESTING – HOW DO YOU DECLARE A WINNER?

Key Takeaways:

When declaring a winner it has to be statistically valid. In other words, there has to be a significant enough difference, that you really set a new course in whatever you do.

To understand the statistical significance of your A/B test you have to remember 3 specific parameters:

Sample Size

Test Size

Confidence

Make sure you’re testing something that can actually have an impact.

A smart and well thought out test is important, you want to learn something, even if you fail.

Below is a lightly edited transcript of Episode 31 of the Inevitable Success Podcast with Damian and special guest Stephen Yu. (Listen Here)

Transcript:

Damian: So in today’s episode Stephen Yu and I are going to be talking about different ways that you can test to improve your marking program. So for example, you know, we’re big proponents of the champion/challenger methodology, basically always having an incumbent winning approach to all of your marketing that you’re constantly challenging and we always prefer to do this in a testable format. Now that said, sometimes the metrics that come back not so clear, sometimes you look at the wrong metrics. So today we want to go a little bit deeper as to how would you determine if you have a winner or not?

Stephen: Of course, and I’ve been saying this for a long time, that test results are not baseball scores. In a baseball match, with the World Series going on right now, well if you won by one run that’s fine. It’s a one-run game, maybe it was a pitchers duel. But in testing it’s not like that, it has to be statistically valid. In other words, there has to be a significant enough difference, that you really set a new course in whenever you do.

Damian:So the takeaway is, just because you have a test that would have won a baseball game doesn’t mean that you actually have a winning idea.

Stephen: I call it conclusive evidence that you have a winner.

Damian: So I’ve totally experienced this myself, you know, I’ve run probably hundreds of tests in order for types of medians at this point. Actually, the most common result that I have found, especially if the test is not aggressive enough, is inconclusive. It’s a very common result. You know, me personally when I’m working on optimizing things, I actually love to go after bold aggressive changes and here’s why. When you’re testing, the fact you’re testing, you’re already managing the risk of rolling out a bad idea.

Stephen: OK. Hopefully, it doesn’t stink too much.

Damian: Well yeah but you’re going to not necessarily roll out to everybody, you can manage that too. But, you know, I love the idea of actually avoiding having inconclusive tests. I either want something to work phenomenally or prove that I should never do that again quickly. And I think when you look for things that can have big changes, the odds of learning nothing and just spinning your wheels go down from there.

Stephen: I think you’re describing is what we call scientific approach.

Damian: Yes.

Stephen: What is the scientific approach? We all know it, we don’t practice it, but we all know it. We took some science classes in school, it’s a social science. In the beginning, there is the hypothesis – if we do this, this will happen, or if you give this drug to somebody they’ll be better, or not one to this person drop the trial, it’s the same method, right? The biggest challenge in any analytics is to come up with a hypothesis. In other words, whatever you test here, that idea should come from human beings.

Damian: Yeah, and to that end, if you design it really well, like you have a null hypothesis too, so even if you fail, you learn something as well. So you know, I think that it’s really important you know, to make sure you’re testing something that can actually have an impact.

Stephen: But having said that, when we talk about baseball for a moment, but sometimes the winning game should be like a baseball match, that if you’re testing some creative, and the difference is slight, maybe a difference in opinion, the cost of being wrong is not that great. Therefore if you had to just pick one, then fine don’t worry too much about statistical validity. Just declare a winner and go bat away. But if you’re testing some different Audience if you will, or the result of not mailing anybody at all and seeing if your mailing is doing any good, in those cases the winners should be declared carefully because you will change the way you acquire your prospect lists, the way you talk to your customers for a foreseeable time, you really want to make sure that what you learn here is something that is sustainable.

Damian: Yeah, and it should be able to be summed up in a quick conversation of what you learned, what you tested –

Stephen: When you say one thing is better than the other, it becomes quite a bit of a history-making endeavor.

Damian: Yes, just to kind of, I think to maybe, you made me think about how to clarify a little bit more – the test that I feel like we need to avoid at all costs as marketers is the imperceptible test. It’s where you if you’re using creative as an example to the same target, it’s when you show two creatives side by side and the average person actually doesn’t know what’s different between the two of them.

Stephen: Yeah the green button, red button test.

Damian: Yeah. What if it’s like, you know, blue button and slightly less blue button right? And I see these tests happen a lot.

Stephen: We have a joke here, we have a lot of developers version of streams. You’ve been in both high-definition streams, the colors are not always the same.

Damian: Exactly, but here’s another example though. You know if it’s a certain slight color blue, eight percent of the population, the male population can’t even see it because they’re colorblind. You know? We actually had a story about this. So now they’re treated like that’s the result.

Stephen: Yeah, so there’s the so what question. In fact, let’s talk about the whole scientific approach. You set up a hypothesis, set up the test rules, execute the test, declare a winner. There’s the last step which is, so what? You always have to end every test with a so what question. So what are we going to do about it? So is this something that you’re going to do forever? Is it that significant? So yeah, I’m using the word significance again.

Damian: Yes let’s dig into that one a little bit.

Stephen: I think we should dig into what statistical significance is for the people who are not stat majors. Simply for non-stat majors, you just have to remember certain parameters that you don’t jump to conclusions too hasty. One is, what is what is a sample size? It’s an easy example, so okay they do the A/B testing and whether the A or B, the difference is three clicks. Well, I don’t even have to test it, three clicks out of how many, about a few thousand. You know what, that’s not a difference.

Damian: You know what, there’s some math into it.

Stephen: Oh there’s some total math into this, but we’re starting out easy.

Damian: There are a couple of like good ways of thinking about this that I’ve approached over the past few years. So sample size, there are some general rules, of course, larger is typically always better. Right? And the other thing too is, if, and I’ll give you a really clear example of this, you can have a small sample size and still get the statistical significance.

Stephen: The difference is bigger.

Damian: Exactly, and that’s what people miss.

Stephen: That’s exactly what I’m talking about. So you’ve got to have all three in your mind. I’ll give you three problems. One is the general sample size. Now people get scared of the large sample for valid reasons. Let’s say you have some holdouts some mailing or emailing holdouts but you’re not going to touch them. Well if you don’t touch them they’re not going to respond. That’s the belief, right? That’s why we do these things. Well, if I have a big holdout sample, I’m going to lose my money making opportunity. That is not a wrong way to see it but you’ve got to still test. So what is a good test size? Again the size matters. Now, I talk about it as a response size, not as a test size. Why? Because now you have to think about what is the typical difference that you’re trying to measure. Are you trying to measure the difference in 0.1%? Or plus or minus 1% is good enough for you.

Damian: So define response in this context.

Stephen: In other words in this context this – and by the way, if you are testing alternate click-through rates, they are normally in double-digit percentages, it’s easy. But in a mailing situation or like the alternate response for it, that is the number of actual conversions divided by the number of touches. That number generally is very small but that’s the ultimate number, isn’t it? Like who cares if you have all these opens if nobody bought it. Because that’s the ultimate barometer of success: is how much actual conversion did you see, and how much money did they bring in? So you even have measurements like revenue generated by a thousand touches and stuff like that. That’s why we have that ultimate merchant, because of the money talks. Now, what is the typical difference between, say you have a sale that you know you would touch and you have a mail sale here and one gets 1.2% response and the other gets like 0.18 difference – is that a real difference? You have to think about the size of the difference that you’re trying to measure, the smaller that you want to see the result, the bigger the sample size. That’s another thing.

Damian: Yep.

Stephen: There’s a third element. How confident do you want to be?

Damian: Confidence.

Stephen: Do you want to be 98% confident all the time, or 95% confidence or even 80% is good enough for you.

Damian:Let’s go a little deeper on that. What is the difference, like practically, between how long you have to wait for 95% confidence versus like 98% confidence?

Stephen: That is at a confidence level most directly related to sample size, at the time that you read. Now it’s slightly related because it could read longer, of course, you have a larger sample. What does not change is that the test you universally created, all that happened in the beginning. Just because you waited longer doesn’t mean that the test universe gets bigger. So this question should be answered before then. So you have to have some idea of the time you are probably going to measure by, you have some idea of what kind of a difference you are going to measure. So they have to know the typical response –

Damian: Right, a range of outcomes.

Stephen: Exactly you’ve got to have some idea that oh, yeah so I want to measure within just a 5% difference in open rate. That’s fine. So these things determine the size of the sample and of course the confidence level is higher figures into it.

Damian: Right. And you know what, one time I actually remember having this conversation and I said, I think I started saying that there was pushback that I either got from a client or somebody that was new here about the sample size being like a truth always, you know, more sample better. And I said, “Just think about it this way. The variance in the range of outcome has a massive impact on how many people you need.” I said, “Go through this thought experiment. Let’s say you’re AB testing two landing pages. The test goes to a fully functioning landing page that you can check out on. The control goes to a 404 dead page. You going to know very quickly you don’t need a high sample size to figure out that one is better than the other.” And that’s such a good logical test to be like, “Oh I understand the math of this.” And that’s powerful when you really understand how this stuff works because then you can start to wrap your head around what you can believe and what you don’t have to.

I mean even in medical testing, there are conditions where they’ll test that one drug is so much more powerful than the other or dangerous, that they end the test early because it’s such, you know, if people start dying then it’s very easy to tell that there’s a problem early. And that’s another thing, ending a test early when you hit a large variance in outcome.

Stephen: Tell you what it ruins baseball. Like you know what, this pitcher stinks, let’s not even continue and further agonize the team. But what you said kind of reminded me of what, a lot of marketers are too greedy about the things that they test. Please don’t do that, because I’ve seen so many tests where they’re testing everything. This source, creatives, segments, and then they go, “Well we’ll just look at all the responders and test group and divide them into all these different cells composed of like three dimensions like this segment, creative.” That’s a lot already right? But that means some cells are big enough by accident, but some cells can be so small we cannot read any result for all those dimensions. Now, when that happens I say go back to economics class again. What is the economic theory proving? We always say things like, “With all things – “

Damian: Of equal.

Stephen: Of equal what is the outcome?

Damian: I’ve got that one. You’re testing me.

Stephen: Now say it again in Latin. I’m just kidding. But the point is, if you do that, then you know what for this report I’m going to only see from a segment point of view, so which segment? Now you may have enough sample responders in it to see the result. And then you, okay so all other things being equal in terms of creative, which one? You could do it that way too.

Damian:Yeah. Tell me if this is what you, I think you might be saying something else, but you made me think of another idea. This is what happens, you just start thinking of past experience and I’m going to share it. So, I remember doing a test early in my career and I think it was for a landing page of some sort, and I remember that just so happened the randomness because random doesn’t mean even when an AB test is routing traffic, okay? And one of the test pages in hindsight had gotten so much more brand traffic than the non-brand traffic. And for anybody that knows search, brand traffic tends to convert much higher than non-brand traffic. Right? Sometimes like 10 to 1, and the slight skew in one part of the experiment, randomly through traffic randomization, when I isolated that after the fact, completely change the results. So that taught me that being able to get a fair target is really important in constructing a test.

Stephen: Oh my god yes. That’s like saying that, I even wrote an article about this, why were all these people dead wrong about predicting Trump winning the election. You know what it was? It was a sampling error. They under sampled a survey of an area. You cannot predict the outcome of an election without fair representation. Think about it, if you just survey a whole bunch of city folks, guess what they are going to say? I mean there’s a regional bias in all of us right? So it was a sampling thing. Also when the sample size was so small, then you are talking about a town with really few people living in it and what if you missed out on a major household by just randomness. The only way to fix it is well, of course, you have to have a fair randomization routine, otherwise, it’s fraudulent.

Damian:Well this is the whole thing like the randomization, this is confidence level, right? Like that the randomization the higher the sample size you have the more confident you can be right?

Stephen: That’s right, that’s a result that the way we say it is that the higher the sample size, it is more likely to resemble the real universe.

Damian: Right.

Stephen: That’s the key. It’s not about being just like the universe or that you have to call everybody and nobody’s going to do that.

Damian: Yeah. Whenever possible, and this is not possible for everybody. But like let’s say if I could do a paid search test, I would try to like I organize, I just want people to type these keywords in, you know? There’s sometimes you can do that but sometimes it makes it so small that unless you think you’re going to get a big variation in outcome, you don’t learn. But this is where really understanding the math of how all these things tie together, can help you figure out what the best thing to test is you know? If you know that you are going to have a smaller sample size and you’re not sure if it’s going to have a big range of outcomes, you may have to take a different testing approach or maybe think about how could you bubble this up into something thematically bigger to test as a bigger idea to a bigger universe because you’re spinning your wheels with inconclusive to low confidence results.

Stephen: Yeah 100 inconclusive small tests don’t mean anything.

Damian: Yeah, well it does. It means you wasted a lot of time, a lot of money.

Stephen: Somebody kept their job by doing busy work, yeah.

Damian: For some period of time.

Stephen: I’ve found that those people are really good at keeping their jobs. I’m being sarcastic. So going back to the baseball analogy let’s just end with a baseball analogy. We started with the whole baseball thing.

Damian: This is America it’s America’s favorite pastime.

Stephen: And it’s the World Series going on. Now, just like baseball analogies, which is by the way statistically significant because there are like over 160 games so that’s why there’s enough number of pitches and hits and walks that we can predict these things right? That’s why during the postseason it’s harder to predict based on just the statistics alone.

Damian: Right, if you’re trying to do the Moneyball at little league would be harder.

Stephen: Now, why are baseball coaches so good at what they do? Because they’re much smarter us? Maybe they are, but the real reason is because they’ve seen everything. When they move certain players in the field, it’s because they’ve seen it before. That means, just like these testers, if you play this game a lot, you’ll get better at it. So, we only talked about rough guidelines today. But, having a testing mindset is the hardest part. A lot of digital marketers just don’t test.

Damian: It’s very freeing I think to embrace testing.

Stephen: I think it is, you don’t want to be wrong.

Damian: You eliminate yourself from the outcome.

Stephen: It’s the math – that’s the way. So I think the hardest thing is having the scientific approach and actually race it, and you actually to commit to a test. And if you’re wrong, don’t give up, do it again. That’s baseball league, you don’t give up after one loss. Just keep at it and you will be better at it, you will think of more dimensions of a test as you do it.

Damian: I also think there’s this intuitiveness that comes from experience in designing test. That’s a hard one to quantify, but over time you will see, you know, everybody has a supercomputer in their head which is our brain. And this is one of the things that, you know, intuition is basically, we’re actually calculating that and figuring out it’s probably on something objective.

Stephen: And the mother of a hypothesis. Think about it. Now, even with all the automated tests scheduling, one thing the machine never determines is what test.

Damian: Right.

Stephen: Sorry that’s coming from you.

Damian: Yeah exactly. So you know you’ll gradually like figure out things that are worth testing and ways to test it where you can learn something win or lose.

Stephen: And the next time you do it you will know the expected response rate and such things so you can design a better test. That’s how it works.

Damian: All right. This was a lot of fun.

Stephen: As always.

Damian: Yeah, it really is. This is a topic that I find incredibly stimulating and a lot of other people do, and I see it done wrong so frequently so I’m glad we spend some time on it.

Stephen: That’s why I call it don’t treat the test result as a baseball game.

Damian: Not a baseball game. All right take care.

Stephen: Thank you.

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